Statistics & Machine Learning for Regression Modeling with R

Regression analysis is one of the central aspects of both statistical and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions.

Apply statistical and machine learning-based regression models to deals with problems such as multicollinearity

Learn when & how machine learning models should be applied

Note: Software not included

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Mining unstructured text data and social media is the latest frontier of machine learning and data science. This course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like the caret and dplyr to work with real data in R.

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Working With Classes: Classify & Cluster Data With R

Harness The Power of Machine Learning for Unsupervised & Supervised Learning in R

In this course, you'll learn to implement R methods using real data obtained from different sources. After this course, you'll understand concepts like unsupervised learning, dimension reduction, and supervised learning.

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Important Details

Length of time users can access this course: lifetime

Access options: web & mobile streaming

Certification of completion included

Redemption deadline: redeem your code within 30 days of purchase

Updates included

Experience level required: beginner

Requirements

Able to operate & install software on a computer

Prior exposure to common machine learning terms such as unsupervised & supervised learning

Pre-Process & Visualize Data With Tidy Techniques in R

Become Highly Proficient in Data Pre-Processing, Wrangling & Visualization Using the Two Most In-Demand R Data Science Packages

With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.

Read-in data into the R environment from different sources

Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Data Pre-Processing & Visualisation Training with R

This course is designed to equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. You'll discover data visualization concepts in a practical manner that will help you apply them for practical data analysis and interpretation. You'll also be able to determine which wrangling and visualization techniques are best suited to specific problems.

Access 51 lectures & 6 hours of content 24/7

Read in data into the R environment from different sources

Carry out basic data pre-processing & wrangling in R Studio

Learn to identify which visualizations should be used in any given situation

Build powerful visualizations & graphs from real data

Note: Software not included

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Time Series Data Analysis With Statistics and Machine Learning

Learn How To Work with Temporal Data Using Statistical Modeling & Machine Learning Techniques In R

In this course, you'll use easy-to-understand, hands-on methods to absorb the most valuable R Data Science basics and techniques. After this course, you'll understand the underlying concepts to understand what algorithms and methods are best suited for your data.

Access 52 lectures & 5 hours of content 24/7

Get an introduction to powerful R-based packages for time series analysis

Learn commonly used techniques, visualization methods & machine/deep learning techniques that can be implemented for time series data

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.